A multi-target tracking system designer encounters decision problems related to track initialization, measurement to track association, track confirmation and track termination as well as possibly employing an m-detection-out-of-n time steps logic. These decisions can be difficult in a dense clutter scenario. Conventionally, these decisions can be made based upon the total number of measurement associations, length of no associations and the total life of the track in question. A decision rule based upon the above quantities can be called a fixed rule. Such a decision rule affects true tracks (i.e., a track on a real or true target) and false tracks (i.e., a track on a target which is not real) in a similar way and also tends to produce inferior results since tracks cannot be discriminated from false tracks. For instance, if a strict decision rule is applied, then the number of false tracks and the average length of the false tracks would decrease. However, the corresponding performance measures for the true tracks would be affected similarly, which is undesirable. On the other hand, if a loose decision rule is applied, then longer average track lives would result for both true and false tracks as well as an increase in the number of false tracks.
The general stages of a tracking method include a track initialization stage, a track maintenance stage (by data association) and a track termination stage. Probabilistic data association (PDA) based methods are a popular approach for the track maintenance stage, which relaxes the otherwise binding constraint of assigning one track to only one measurement and weighs the contribution of each measurement by the probability that it is target originated. Another group of tracking methods, unlike the PDA tracking methods, retains the single track to single measurement association constraint. These methods are known as assignment based methods since they use assignment or global nearest neighbor approaches to associate measurements to tracks. Since different measurements are associated with different targets, these methods avoid the track coalescence problem that occurs in closely spaced target scenarios. However, the assignment-based methods can have higher performance uncertainty than the PDA based methods due to the hard decision requirement in the former.